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Crop Yield Prediction using Machine Learning: A Review of Recent Approaches

by Pankaj, P.K. Bharti, Brajesh Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 24
Year of Publication: 2023
Authors: Pankaj, P.K. Bharti, Brajesh Kumar
10.5120/ijca2023922994

Pankaj, P.K. Bharti, Brajesh Kumar . Crop Yield Prediction using Machine Learning: A Review of Recent Approaches. International Journal of Computer Applications. 185, 24 ( Jul 2023), 27-32. DOI=10.5120/ijca2023922994

@article{ 10.5120/ijca2023922994,
author = { Pankaj, P.K. Bharti, Brajesh Kumar },
title = { Crop Yield Prediction using Machine Learning: A Review of Recent Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 24 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number24/32841-2023922994/ },
doi = { 10.5120/ijca2023922994 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:58.426614+05:30
%A Pankaj
%A P.K. Bharti
%A Brajesh Kumar
%T Crop Yield Prediction using Machine Learning: A Review of Recent Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 24
%P 27-32
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Machine learning is an important tool for the prediction of crop yield. The prediction of yield can help the farmers as well as the policymakers to take timely decisions. With the advanced information on estimated yield, the farmers can make decisions on what to grow to meet the requirement of a growing population. Machine learning techniques can make better yield predictions based on the patterns and correlation information in images or data. There are several machine learning algorithms tested for crop yield prediction. In this work, the recent research works are analyzed in terms of algorithms and the type of information used in prediction studies. It is observed that deep learning techniques have achieved remarkable success in recent times. Most of such methods are based on images of different types such as color, multispectral, and hyperspectral images. This work presents a brief review of the machine learning techniques used for crop yield prediction. The major characteristics and challenges of the methods are discussed and research gaps are identified.

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Index Terms

Computer Science
Information Sciences

Keywords

Crop Yield Prediction Machine Learning Approaches Multispectral Images Algorithmic Classification Chronological Review

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